📊 HTML Report Generation with IQR Outlier Detection

📌 Project Objective

This Python program:

• Accepts a CSV file as input
• Performs Interquartile Range (IQR) outlier analysis on a selected column
• Splits the dataset into outliers and non-outliers
• Generates an interactive HTML report with:
  • File and column information
  • Summary statistics
  • Histograms and boxplots for all subsets
• Saves the outlier and non-outlier subsets into separate CSV files

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🧠 Skills Learned

• Parsing and preprocessing CSV files using Python
• CLI-based tool development for data analysis tasks
• Outlier detection using the IQR method
• Generating statistical summaries: mean, median, std, IQR
• Creating plots using matplotlib
• HTML report generation with Jinja2 templating
• Exporting datasets as new CSV files (outliers/non-outliers)

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🧰 Libraries & Technologies

• pandas: Data manipulation
• numpy: Numerical calculations
• matplotlib: Data visualization (boxplots, histograms)
• jinja2: HTML templating
• sys: Command-line argument handling

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🚀 Steps Performed

1. Import required libraries
2. Read command-line arguments
3. Load CSV file
4. Extract target column
5. Perform IQR-based outlier detection
6. Calculate summary statistics
7. Split data into outliers and non-outliers
8. Generate boxplots and histograms
9. Create and save a dynamic HTML report
10. Export filtered datasets to CSV

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🖼️ Screenshots

📈 Dataset Summary

📉 Histograms & Boxplots

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📂 Output Files

• outliers.csv: Contains outlier records
• non_outliers.csv: Contains clean data
• report.html: A styled HTML report with stats and plots

⚙️ How to Run

python detect_outliers.py input.csv 2